Handoff effect on PRMA (Packet Reservation Multiple Access) in micro-cellular system

PRMA(Packet Reservation Multiple Access) has been proposed for third generation wireless information network by Goodman et al. [5] [4]. Due to small micro cell radius mobile initiated handoff has been proposed to disperse the burden of BS(Base Station) [14]. Even though these frequent handoffs will not burden on BS, increased contends due to handoff will affect the over all performance of PRMA. In this paper, we analyze the handoff effect on PRMA performance under micro-cellular system. Steady state speech terminal model with handoff is proposed.. Stabilities are derived based on proposed steady state terminal model[F(cs)=M] and also increased contend [F(ch)=M] due to handoff. The multiple EPA(equilibrium) points change with handoff. Packet dropping probability and data packet delay are calculated using both Markov Analysis and backlog b from F(cs)=M and F(ch)=M. The changes of performance under handoff show the need of handoff schemes at PRMA

3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection

Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large domain gap between the two sensors in terms of coordinates and data distribution when fusing their features. In this paper, we propose a novel camera-LiDAR fusion architecture called, 3D Dual-Fusion, which is designed to mitigate the gap between the feature representations of camera and LiDAR data. The proposed method fuses the features of the camera-view and 3D voxel-view domain and models their interactions through deformable attention. We redesign the transformer fusion encoder to aggregate the information from the two domains. Two major changes include 1) dual query-based deformable attention to fuse the dual-domain features interactively and 2) 3D local self-attention to encode the voxel-domain queries prior to dual-query decoding. The results of an experimental evaluation show that the proposed camera-LiDAR fusion architecture achieved competitive performance on the KITTI and nuScenes datasets, with state-of-the-art performances in some 3D object detection benchmarks categories.Comment: 12 pages, 3 figure

Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks

With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%

Organizational routines and regional industrial paths: the IT service industry in the US National Capital Region

<p>This study aims to address how organizational routines could affect the business activities and regional industrial paths of organizations by conducting a case study of the information technology (IT) service industry in Fairfax County in the US state of Virginia. Specifically, it analyzes the interwoven processes among three factors in the evolution of a knowledge-based regional industry, namely, organizational routines of local IT service firms, industrial knowledge bases and regional specificity. Empirical results explain the mechanisms of the adaptation processes of heterogeneous actors when two groups of disparate market-oriented routines exist. The integrated framework linking these three factors can provide explicit perspectives for understanding the context-specific evolution of clusters.</p

Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning

Unlike previous dialogue-based question-answering (QA) datasets, DREAM, multiple-choice Dialogue-based REAding comprehension exaMination dataset, requires a deep understanding of dialogue. Many problems require multi-sentence reasoning, whereas some require commonsense reasoning. However, most pre-trained language models (PTLMs) do not consider commonsense. In addition, because the maximum number of tokens that a language model (LM) can deal with is limited, the entire dialogue history cannot be included. The resulting information loss has an adverse effect on performance. To address these problems, we propose a Dialogue-based QA model with Common-sense Reasoning (DQACR), a language model that exploits Semantic Search and continual learning. We used Semantic Search to complement information loss from truncated dialogue. In addition, we used Semantic Search and continual learning to improve the PTLM&rsquo;s commonsense reasoning. Our model achieves an improvement of approximately 1.5% over the baseline method and can thus facilitate QA-related tasks. It contributes toward not only dialogue-based QA tasks but also another form of QA datasets for future tasks

exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)

News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models